COMPENSATION THROUGH PREDICTION FOR ATMOSPHERIC TURBULENCE EFFECTS ON TARGET IMAGING AND HIGH ENERGY LASER BEAM

Atmospheric turbulence significantly degrades the performance of High Energy Laser (HEL) beams. The three key undesirable effects are: (1) degraded target images used for target tracking; (2) inaccurate HEL pointing; and (3) reduction in HEL power during propagation to the target. The current approach for compensating for these turbulence effects uses adaptive optics to measure atmospheric turbulence and compensate the aberration in the optical beam. However, an adaptive optics system has limited performance in strong turbulence and an optical system makes the HEL system more complex. With improvements in Deep Learning algorithms and further development in Artificial Intelligence, we used Deep Learning and Convolutional Neural Networks to predict the atmospheric turbulence and compensate for its negative effects on laser beams. The predicted turbulence can be used for image correction and HEL beam correction using a deformable mirror to reduce turbulence effects during propagation.Military Expert 5, Republic of Singapore NavyApproved for public release. Distribution is unlimited